This inventor holds 1 USPTO granted patent and 1 EPO patent. Top assignee: Schlumberger Technology Corporation. Active years: 2026.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:
Years Active: 2026
Title: Prashanti Deviani: Innovator in Electric Submersible Pump Technology
Introduction
Prashanti Deviani is a notable inventor based in Pekanbaru, Indonesia. She has made significant contributions to the field of electric submersible pumps (ESPs) through her innovative patent. Her work focuses on utilizing machine learning to enhance the reliability and efficiency of ESPs.
Latest Patents
Prashanti holds a patent for an Automated Electric Submersible Pump (ESP) failure analysis. This invention involves a method, apparatus, and program product that utilizes data associated with one or more electric submersible pumps to train a machine learning model. The model can perform failure analysis on ESPs, and features from the data can be encoded into a machine-readable format to facilitate ingestion by the machine learning model. This advancement is crucial for improving the operational efficiency of ESPs in various applications.
Career Highlights
Prashanti is currently employed at Schlumberger Technology Corporation, a leading company in oilfield services. Her role involves applying her expertise in machine learning and pump technology to develop innovative solutions for the industry. With her background and skills, she has positioned herself as a key player in the field of ESP technology.
Collaborations
Prashanti has collaborated with notable colleagues, including John Pang and Alfredo De La Fuente. These partnerships have allowed her to leverage diverse expertise and drive innovation in her projects.
Conclusion
Prashanti Deviani's contributions to the field of electric submersible pumps through her innovative patent demonstrate her commitment to advancing technology. Her work not only enhances the reliability of ESPs but also showcases the potential of machine learning in industrial applications.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile